Integration of Machine Learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We report results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic emergency braking demonstrator running in an industry-grade simulator. We present the outcome of applying AMLAS on SMIRK for a minimalistic operational design domain, i.e., a complete safety case for its integrated ML-based component. Finally, we report lessons learned and provide both SMIRK and the safety case under an open-source licence for the research community to reuse.
翻译:正在制订新的安全标准和技术准则,以支持基于ML的系统的安全,例如汽车领域的ISO 21448 SOTIF和自动系统框架内使用的机器学习保证,SOTIF和AMLAS提供了高级别指导,但细节必须针对每个具体案例加以说明。我们报告了工业-学术界合作在SMIRK的安全保障方面取得的成果,SMIRK是一个以ML为基础的行人自动制动模拟器,在工业级模拟器中运行。我们介绍了在SMLORK上应用AMLAS作为最低操作设计领域的完整安全案例的结果,即基于ML的一体化组成部分。最后,我们报告了经验教训,并在开放源许可证下向研究界提供了SMIRK和安全案例,供研究界重新使用。